Generative-AI-in-Organizations-Refresh-Harnessing the value of generative AI

Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors Harnessing the value
of generative AI
2
nd
edition: Top use cases across sectors
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Table of
Content
2Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

3Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Executive
Summary
Increased maturity and investment: Organizations are
embracing generative AI, which is reflected in an uptick in
investment levels. The vast majority (80%) of organizations
in our survey have increased their investment in generative
AI from 2023, 20% have maintained their investment level,
and not one organization decreased their investment from
last year. Larger enterprises lead the charge, and overall,
nearly one-quarter (24%) of organizations have integrated
this technology into some or most of their operations, an
acceleration from 6% reported just 12 months ago. This
increase in generative AI adoption since 2023 spans all
sectors. For example, in retail, implementation increased to
40%, more than doubling from 17% in 2023.
Pervasive integration across functions: Generative AI
permeates organizations, catalyzing a shift in operational
paradigms. In the past year, there has been an increase in
its adoption across all organizational domains, from sales
and marketing to IT, operations, R&D, finance, and logistics.
Moreover, generative AI adoption among employees is robust
in most organizations, with the majority allowing its use. Only
3% of surveyed organizations enforce a complete ban on
public generative AI tools in the workplace.
Tangible benefits and strategic shifts: Early adopters
of generative AI are are seeing benefits in areas in which
generative AI has been piloted or deployed, from improved
operational efficiency to enhanced customer experience.
For example, on average, organizations realized a 7.8%
improvement in productivity and a 6.7% improvement
in customer engagement and satisfaction, over the past
year. Organizations anticipate that generative AI will drive
adjustments to their strategic approaches and business
models. With a belief that the technology will be a key
driver of revenue growth and innovation, organizations are
exploring new ways of harnessing its capabilities.
The rise of AI agents: The emergence of AI agents marks a
shift, with potential to enhance automation and productivity
across sectors, business processes, and along the entire
value chain. These AI agents have evolved from supportive
4Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Executive
Summary
tools to autonomous entities capable of executing tasks
independently. Organizations are eager to adopt AI agents,
with a strong majority (82%) intending to integrate them
within 1–3 years. There is a level of trust in AI agents for
specific tasks, such as generating work emails, coding, and
data analysis. However, organizations are also mindful of the
need to establish guardrails to validate AI-made decisions,
ensuring transparency and accountability.
Empower your generative AI journey: Organizations should:
Establish a robust framework for data governance
and management
Strengthen the data platform and cultivate trust to
ensure reliable outcomes
Cultivate expertise through strategic training and
talent development
Acquire understanding and expertise of the generative
AI ecosystem
Deploy a generative AI platform to manage use cases
at scale
Fortify against cybersecurity threats
Embrace emerging trends such as AI agents to boost
competitiveness and innovation.
5Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Gen AI in organizations - annual research
Gen AI for management
Data mastery
Special edition of our premium journal Conversations for tomorrow on Gen AI
Gen AI in supply chain
Gen AI in manufacturing
Gen AI for marketing
Gen AI for software
engineering
Gen AI in R&D
and engineering
Gen AI in business operations Gen AI in customer service
Gen AI and
consumers
Gen AI and
sustainability
Gen AI and
ethics/
trust
Gen AI and
cybersecurity
This report is a part of Capgemini Research Institute’s series on Generative AI
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To find out more, please go to https://www.capgemini.com/insights/research-institute/
*Upcoming reports
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Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Who should
read this
report and
why?
This report offers an overview of the transformative
potential of generative AI for large organizations
across sectors such as automotive, consumer
products, retail, financial services, telecom, energy
and utilities, aerospace and defense, high tech,
industrial manufacturing, pharma and healthcare,
and the public sector/government. It is the second
installment in an annual research series and identifies
shifts in trends from 2023.
The report will help business executives identify use cases
that illustrate the applications of generative AI across
functions, including IT, sales, marketing, and product design/
R&D. The report draws on the comprehensive analysis of a
survey of 1,100 leaders (director level and above) across 14
countries. Finally, it offers recommendations for business
leaders to accelerate their organizations’ generative AI
journeys.
7Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Introduction
Generative AI is rapidly transforming the way we interact with
technology. Machines are now capable of mimicking creative
human thought processes and synthesizing tailored content
of increasingly high quality. This has significant implications
for driving innovation, operational efficiency, and growth.
This report is the second in the Capgemini Research
Institute’s annual research series that examines generative
AI trends and use cases. In the first report of this series,
“Harnessing the value of generative AI: Top use cases
across industries,” we explored the transformative potential
of generative AI, highlighting the function- and sector-
specific use cases with the greatest potential, and comparing
adoption rates across sectors. In our 2023 research, we
discovered that, while still in its infancy in terms of scaled
adoption and implementation, generative AI was on the
agenda in 96% of boardrooms globally. We found that nearly
60% of organizations said their leaders are strong advocates
of generative AI, and only 39% were taking a “wait-and-
watch” approach.
This year’s research highlights a quickening of the pace
of implementation. Notably, nearly one-quarter (24%) of
organizations are now integrating generative AI into some
or most of their locations or functions, up from just 6% in
2023. In this year’s report, we analyze shifts in generative
AI adoption and take a closer look at the investment levels
and benefits organizations have realized. We also turn the
spotlight on AI agents, a quickly evolving technology with
potential to drive innovation.
To gauge perceptions of generative AI, we conducted a
global survey of 1,100 executives at organizations with
annual revenue above $1 billion. We invited public-sector
organizations and government entities with an annual
budget of at least $50 million to participate. Organizations
are headquartered in 14 countries: Australia, Canada, France,
Germany, India, Italy, Japan, the Netherlands, Norway,
Singapore, Spain, Sweden, the UK, and the US. Organizations
surveyed operate across 11 key sectors: aerospace and
defense, automotive, consumer products, energy and
utilities, financial services, high tech, pharma and healthcare,
industrial manufacturing, retail, telecom, and the public
sector/government. For more details on the survey sample,
please refer to the research methodology.
8Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Introduction
This report comprises five sections:
Organizations are
deploying generative
AI at pace
1
Generative AI
is pervading
organizations
2
Generative AI is
already driving
benefits
3
AI agents: The new
technology frontier
4
How organizations
can accelerate
their generative AI
journeys
5
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Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations are deploying
generative AI at pace 01
10Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Investment in generative AI
is increasing
According to our research, 80% of organizations have
increased their investment in generative AI from last year.
Remarkably, not a single organization decreased their
investment, while the remaining 20% maintained the same
investment level. This trend echoes across all sectors and
organization sizes in the research. For example:
• In aerospace and defense, almost nine in 10 organizations
have (88%) boosted their investment in generative AI;
• Within retail, the lowest proportion among all sectors in
our survey, 66% have invested in generative AI;
• 73% of organizations with $1–5 billion in annual
revenue have increased their investment, and 89% of
organizations with over $20 billion in revenue have
done so.
Dave Chen, Head of Global Technology Investment Banking
at Morgan Stanley, says:“Though cost savings and operational
efficiencies remain a priority for large enterprises, they are also
showing a willingness to spend, especially on generative AI and
traditional AI hardware and software that may [in the medium
to long term] help reduce costs and increase productivity and
revenue.”
1
Recently, The Coca-Cola Company committed
$1.1 billion over a five-year period to accelerate cloud and
generative AI initiatives.
2
80
%
of organizations have increased their
investment in generative AI from last year
11Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI
executive survey, May–June 2024, N = 940 organizations that are at least exploring generative AI capabilities.
*In the 2024 data points respondents from India and the public sector/government are excluded as they were not
included in the 2023 research.
Figure 1.
More organizations have increased their maturity in generative AI % of organizations who agree with the statement on generative AI maturity
2023
2024
We have started exploring the
potential of generative AI
We have begun working on some
pilots of generative AI initiatives
We have enabled generative AI capabilities
in some of our functions/locations
We have enabled generative AI capabilities in
most/all of our functions/locations
53% 41% 6%
27% 49% 18% 6%
In the past year,
implementation of
generative AI has
accelerated
Across our total survey sample of 1,100 organizations,
only 6% of organizations are yet to begin exploring
generative AI. Out of those who have at least begun to do
so (n=1,031), nearly one-quarter (24%) are now integrating
this technology into some or most of their locations or
functions. This marks an increase from just 6% reported last
year, indicating widespread recognition of the benefits (see
Figure 1).
Only 10% of smaller organizations in our research,
with annual revenue of $1–5 billion, have implemented
generative AI across some or most of their locations
and functions. In contrast, 49% of organizations with
annual revenue surpassing $20 billion have implemented
the technology.
Jack Forestell, Chief Product and Strategy Officer at Visa,
says: “While much of generative AI so far has been focused on
tasks and content creation, this technology will soon not only
reshape how we live and work, but it will also meaningfully
change commerce in ways we have yet to fully understand.”
3
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Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

% of organizations who agree with the statement on generative AI maturity, by sector
Average RetailIndustrial
manufacturing
Consumer
products
AutomotiveHigh techFinancial
services
Energy and
utilities
TelecomPharma and
healthcare
Public
sector
Aerospace
and defense
We have started exploring the
potential of generative AI
We have begun working on some
pilots of generative AI initiatives
We have enabled generative AI
capabilities in some of our
functions/locations
We have enabled generative AI
capabilities in most/all of our
functions/locations
20232024 2024*20232024202320242023202420232024202320242023202420232024202320242023202420232024
6%
41%
53%
17%
48%
45%
55%
32%
4%5% 4%
7%
18%
68%54%
47%
53%
21%
33%
3%
49%
4%
10%
14%
40%
11%
11%
51%
7%
37%
2%
20%
48%
3%
44%
2%
11%
68%
2%
10%
68%
3%6%
15%
9%
19%
53%
35%
26% 28%
30%
61%
39%
68%
21%
14%
22%
47%
24%
64%
35%
47%
27%
53%
30%
53%
19% 20%
24%
45%
34%
22%
18%
49%
11%
27%
6%
18%
49%
All sectors have progressed
in their implementation of
generative AI
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities; N varies per sector use case ranging from 50 to 189. *Respondents from the public sector were not included in the 2023 research. **Excluded from Figure 2 is the percentage of
organizations that have started exploring the potential of generative AI.
Figure 2.
Over the past year, there has been an increase in the maturity of generative AI across sectors
Generative AI integration has increased across all sectors
since 2023. For example, 40% of organizations in the retail
sector have implemented generative AI across some or most
functions/locations, more than doubling from 17% in 2023
(see Figure 2). For a detailed listing of use cases by sector and
associated implementation data, please refer to Appendix.
13Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

David M. Reese, Executive Vice-president and Chief
Technology Officer at Amgen, a US biopharmaceutical
company, says: "Generative AI is transforming drug discovery
by allowing us to build sophisticated models and seamlessly
integrate AI into the antibody design process.''
4
Natarajan
Chandrasekaran, Chairperson of Tata Group, states: “In
e-commerce, generative AI is being used to generate product
catalogs, deliver conversational shopping experiences, and
provide personalized offers.”
5
Michael Smith, Chief Information
Officer at The Estée Lauder Companies, says: “The generative
AI chatbot developed with [a partner] helps us quickly find
products that address concerns relevant to specific regions and
emerging markets. We will continue to explore other ways we
can harness the wealth of data across products, ingredients, and
more, in tandem with the power of generative AI.”
6
Highlighting the gradual scaling of generative AI in financial
services, Asim Tewary, former Chief AI Officer, PayPal,
comments: "You have to be able to explain why certain decisions
were made — why a credit limit was set at that amount, for
example. There’s an absoluteness that’s expected from regulators
about being able to explain how the decision was made. Anytime
you impact the consumer or introduce a system risk, regulators
get very concerned.” Also concerning to the financial sector is
the increased sophistication of deepfakes which can deceive
customers into transferring funds to seemingly legitimate
accounts, whether virtual or human agent mimicked. This
underscores the imperative of ensuring both accuracy in
customer-facing AI products and robust security measures in
customer interactions.
7
14Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Investment in generative AI
increases with organization size
On average, organizations surveyed have
allocated around $110 million to generative AI
for the current fiscal year. Thirty-four percent of
organizations in our survey have allocated $50
million or less to generative AI, while 11% have
allocated $250 million or more.
The amount of generative AI investment increases
with company size. For example, on average,
organizations with annual revenue of $1–5 billion
allocate $85 million and those with more than $20
billion allocate $158 million (see Figure 3).
Given that the amount of investment allocated
to generative AI increases with organization size,
larger organizations widely agree that generative
AI is more than just a passing trend, serving as a
pivotal cornerstone in their enterprise evolution.
Large organizations are increasingly embracing
the transformative potential of generative AI
technology more fervently than perhaps other
recent technological advancement. A striking 71%
of organizations with annual revenues exceeding
$20 billion believe that failing to adopt generative
AI will place them at a considerable disadvantage
relative to their competitors. This sentiment is lower
among organizations with annual revenues under $5
billion (56%)
8
.
Our average investment of $110 million is higher than
some recently published analyst reports and likely
reflects the broad categories of enterprise spend
including hardware, software, licensing, training,
among others. According to Gartner, software
development is the function with the highest rate
of investment in generative AI, followed closely by
marketing and customer service.
8
Additionally, a
significant majority of organizations are investing in
partnerships with external providers of generative
AI applications. According to our research, 70%
of organizations are exclusively using external
applications or a combination of external and
in-house solutions. Commonly used tools include
OpenAI’s ChatGPT, GitHub Copilot, Scribe, Microsoft
Copilot, and AWS Gen AI.
Source: Capgemini Research Institute, AI executive survey, May–June
2024, N = 981 organizations who are at least exploring generative AI
capabilities, excluding the public sector.
Figure 3.
Investment in generative AI trends upward with organization size
Average investment dedicated to generative AI,
by annual revenue, 2024
Average $1 bn–
$4.9 bn
$5 bn–
$9.9 bn
$10 bn–
$19.9 bn
More than
$20 bn
$109.8
$85.2
$131.5
$157.7
$99.5
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Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Generative AI is pervading
organizations 02
16Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Generative AI makes
inroads across functions
Our research indicates an increase in generative AI
adoption across all facets of organizations over the past 12
months. Examining individual functions, in the IT domain
for example, the adoption rate has increased to 27% from
4% the previous year (see Figure 4). Hitachi successfully
integrated generative AI tools by leveraging its detailed
system-design knowledge with Microsoft’s AI services,
achieving a 70%–90% success rate in generating application
source code.
9
For a detailed listing of use cases by function
and associated implementation data, please refer to
the Appendix. Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey,
May–June 2024, N = 1,031 organizations that are at least exploring generative AI capabilities; N varies per functional use case, ranging
from 499 to 716.
*ESG/sustainability and human resources were excluded from the 2023 research.
** “Implementation” refers to organizations that have partially scaled the functional use case in question.
***In the 2024 averages, respondents from the public sector and India are excluded, as they were not included in the 2023 research.
Figure 4.
Over the past year, adoption of generative AI has grown across functions
% of organizations implementing generative AI use cases, by function
IT
Risk management
Logistics
Sales/customer operations
Finance
Product design/R&D
Marketing
Human resources*
ESG/sustainability*
4%
4%
2%
4%
5%
3%
2%
27%
26%
26%
25%
25%
24%
22%
19%
19%
2023 2024
17Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Generative AI is used by
employees in most
organizations
The majority of organizations allow employees to use
generative AI in some capacity. Over half of organizations
(54%) require employees to follow specific guidelines when
using these tools, rather than imposing a complete ban.
Three percent of surveyed organizations report a ban on
public generative AI tools in the workplace. However, 7% of
organizations permit unrestricted use of such tools, which
may pose future risks to the organization (see Figure 5).
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities.
Figure 5.
97% of organizations allow employees to use generative AI in some capacity
% of organizations who agree with the statements
2024
54%
36%
7%
3%
We allow all our employees to use generative AI tools but have set up
guardrails/principles
We allow only a carefully chosen group of skilled employees, primarily
those in specialized/technical roles, to use generative AI tools
We allow all our employees to use generative AI tools at will
We have banned all our employees from using public generative AI
tools in the workplace
18Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Aerospace
Sector Company Function Example
Airbus Manufacturing
Has transformed its operations and innovation processes with generative AI. AI assistants
provide aircraft manufacturing instructions, enhancing accessibility to technical data, and
facilitating precise task guidance.
11

Automotive Toyota Product design/R&D
Uses generative AI to incorporate engineering constraints into vehicle design, and
optimizing metrics such as aerodynamic drag, enhancing eﬃciency of electric-vehicle
(EV) design.
12
Automotive Mercedes-Benz Customer experience/
service
Leverages generative AI in its “Hey Mercedes” feature, which is in beta with 900,000
users. It oﬀers personalized, screen-free interactions, enhancing driving experience with
dynamic adjustments and real-time safety support.
13
Consumer products General Mills Customer experience/
service Launched MillsChat, a generative AI tool, to streamline customer service. This enhances
eﬃciency, provides personalized assistance, and encourages customer engagement.
14
Consumer products PepsiCo
Marketing and
branding
Harnesses generative AI to analyze customer feedback, which it uses to reﬁne shape design
and ﬂavor of its Cheetos branded snack, boosting market penetration by 15%. This strategy
has also shortened product launch cycles and increased proﬁtability.
15
Despite the implementation of guardrails and policies to
regulate the use of generative AI, unauthorized usage among
employees is still relatively common. Among the 39% of
organizations with a ban or limitation policy, half of them
say there is still unauthorized usage of generative AI in the
workplace. Amazon has prohibited employees from using
third-party generative AI tools, such as OpenAI's ChatGPT,
particularly for handling confidential data. This policy is
intended to prevent data-ownership issues and protect
sensitive company information.
10
Continue to the next page...
In the table that follows, we highlight recent
generative AI use cases across sectors and functions:
19Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Consumer product
Sector Company Function Example
Unilever Legal
Uses generative AI to streamline legal processes, including research, drafting, and
contract reviews. This allows legal teams to focus on strategic tasks, enhancing
operational eﬀectiveness.
16

Energy British
Petroleum (BP)
Human resources –
employee productivity
Leverages generative AI to assist employees with daily tasks such as email management.
Enhances employee productivity and transforms business workﬂows.
17
Financial services Morgan Stanley Customer
experience/service
Used GPT-4 to create an AI tool for ﬁnancial advisors, allowing rapid access to internal
research. This enhances advisor eﬃciency and client service, simulating top investment
experts on call.
18
High tech BrainBox AI ESG/sustainability
Uses generative AI to reduce carbon footprint of commercial buildings. Uses historical data
to predict interior building temperatures, cutting heat, ventilation and air-conditioning
(HVAC) energy costs by up to 25% and greenhouse gas (GHG) emissions by 40%.
19
Industrial manufacturing Rockwell Automation
IT – coding/software
development
Added generative AI into FactoryTalk Design Studio to help engineers generate code using
natural language prompts, automating routine tasks and improving design eﬃciency. It will
also empower experienced engineers to accelerate development and mentor newcomers
more eﬀectively.
20
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20Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Industrial manufacturing
Sector Company Function Example
Schneider Electric Finance and accounting
Integrated generative AI into Finance Advisor, a conversational assistant for ﬁnancial
analysts in global ﬁnance, ensuring precise, compliant, and timely decision-making across
accounting and related functions.
21
Logistics UPS Marketing
Developed the Message Response Automation (MeRA) system in-house using publicly
available large language models (LLMs), to automate routine customer interactions,
reducing email handling times by 50% and allowing human agents to focus on more
complex issues. It also streamlines operations and improves customer satisfaction by
ensuring prompt, accurate responses.
22
Pharmaceutical
and biotech
Insilico
Medicine
Product design/R&D Identiﬁed a new drug candidate, MYT1, using its generative AI platform in each step of its
preclinical drug-discovery process, oﬀering more eﬀective, safer treatments for breast and
gynecological cancers.
23
Pharmaceutical
and biotech
Moderna Research and
development
Uses generative AI tools, including the company's Dose ID GPT, which uses ChatGPT
Enterprise's Advanced Data Analytics feature to further evaluate the optimal vaccine dose
selected by the clinical study team.
24
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21Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Public sector
Sector Company Function Example
California Department of
Transportation and
Department of Tax and
Fee Administration
Citizen services
Plans to use generative AI to analyze traﬃc data to improve road safety, reduce
call-center wait times, aid non-English speakers, and streamline healthcare-facility
inspections.
25

Public sector State of Pennsylvania Employee operations Runs a generative AI pilot program for state employees, integrating the technology into
government operations. Supports crafting/editing copy, updating policy language,
drafting job descriptions, and generating code.
26
Retail ASOS Sales Uses generative AI to make fashion recommendations, customer-service interactions, and
trend analysis, enhancing user engagement, personalizing customer experiences, and
optimizing retail strategies.
27
Retail Walmart ESG/sustainability
Uses generative AI to reduce food waste by helping employees make quick decisions.
Employees scan a product such as produce or apparel, and a digital dashboard makes
suggestions on what to do with the product based on its characteristics (e.g., ripeness,
whether its seasonal). Suggested actions could include a price change, putting it on sale,
sending the item back, or donating it.
28
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22Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Retail
Sector Company Function Example
Baskin Robbins Product design/R&D
Uses generative AI, in its Seoul, South Korea, store to develop innovative ice-cream
ﬂavors. Supported the introduction of a monthly exclusive ﬂavor, and personalized
experience oﬀered through an ice-cream docent program.
29

Telecom AT&T Employee productivity
Employs generative AI capabilities in its Ask AT&T tool to support employees by
enhancing productivity and creativity, translating documents, optimizing networks, and
summarizing meetings. This leads to improved eﬃciency and greater innovation.
30
Telecom Vodafone Customer
experience/service
Voxi by Vodafone, a generative AI self-service experience, enhances interactions, provides
personalized assistance, and optimizes customer support services, fostering satisfaction
and engagement.
31

23Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations must
carefully scale generative
AI initiatives with a focus
on environmental
sustainability
Organizations must assess technological
advancements in generative AI alongside their
environmental consequences. They should
evaluate the business value, considering
implementation complexity and costs, while
also scrutinizing environmental impacts such
as GHG emissions, electricity usage, and water
consumption. Our research indicates that, roughly
a third of organizations are currently monitoring
energy and water consumption, as well as carbon
emissions, associated with their generative AI
initiatives (see Figure 6).
Source: Capgemini Research Institute, Generative AI executive survey, May–June
2024, N = 1,031 organizations that are at least exploring generative AI capabilities.
Figure 6.
36% of organizations are currently tracking carbon emissions from generative AI use
Carbon
emission
Energy
utilization
Water
consumption
Yes
No
Unsure/ don’t know
% of organizations currently tracking and measuring the
below metrics in the use of generative AI
36%
54%
10%
30%
57%
13%
29%
62%
9%
24Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Furthermore, our research explored the
measures organizations are implementing
to mitigate the environmental impact
of generative AI. Slightly over half of
organizations (54%) are developing guidelines
for responsible use of generative AI, and
47% are transitioning to more energy-
efficient hardware. However, the proportion
of organizations undertaking additional
actions such as investing in renewable energy,
offsetting emissions through carbon credits,
and optimizing training algorithms remains
low (see Figure 7).
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities.
Figure 7.
Half of organizations are currently developing guidelines for generative AI use
% of organizations currently trying to mitigate the carbon footprint from using generative AI
Developing guidelines for responsible use 54%
47%
37%
36%
31%
Using more energy-eﬃcient hardware
Oﬀsetting through carbon credits
Investing in renewable energy
Optimizing our training algorithms
Only a third of organizations are
currently monitoring energy and water
consumption, as well as carbon emissions,
associated with their generative AI
initiatives.
25Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Generative AI is already
driving benefits 03
26Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations have
achieved benefits
Our current research evaluates the benefits that generative
AI has brought at organizational level in the past year in the
areas in which generative AI has been piloted or deployed.
For example, on average, organizations realized a 7.8%
improvement in productivity and a 6.7% improvement in
customer engagement and satisfaction over the past year
(see Figure 8).
Generative AI technology has only been mainstream for
roughly 18 months, and consequently, organizations
are cautiously optimistic about the potential benefits of
integrating generative AI into their strategies and processes.
However, given the advancement and increasing integration
of this technology within all functions, it is expected that the
actual benefits will exceed organizations’ expectations.
Moreover, our research reveals benefits that largely originate
from pilots or partial scale implementations of generative AI.
Benefits are poised to amplify as more organizations adopt
generative AI at full scale across all operational domains.
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 940 organizations that
are at least exploring generative AI capabilities.
Improved productivity: Leveraging generative AI to optimize and improve the performance of existing resources,
such as machines and employees. Increase in operational efficiency: Applying generative AI to pinpoint areas
of waste and inefficiency, thereby reducing the time employees spend on non-value-added activities or
inefficient processes.
*Question asked: What benefits have you already achieved at an organizational level from generative AI, within the
past one year?
Figure 8.
Generative AI yielded benefits in the past year in the areas in which the technology has been piloted or deployed
Average beneﬁts realized from generative AI within the past year
Improved
productivity
7.8%
Improved customer
engagement and
satisfaction
Increase in
operational
eﬃciency
Increase in
sales
Decrease in
cost
Beneﬁt achieved over the past year
6.7%
5.4%
4.4%
3.6%
27Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Magnus Östberg, Chief Software Officer at Mercedes-Benz
AG, discusses the benefits of generative AI on the in-car
experience: "The Mercedes-Benz user experience of tomorrow
will be hyper-personalized. With generative AI, our MBUX Virtual
Assistant brings more trust and empathy to the relationship
between car and driver. Thanks to our chip-to-cloud architecture,
our future vehicles will provide customers with exactly what they
need, when they need it.”
32
Florian Tué, Head of Procurement Transformation at
Carrefour, comments: “We realized comparing three quotes
would take around 30 minutes for a buyer to do manually. If we
do it with ChatGPT and the PoC [proof of concept] that we've
been running, it takes only 10 minutes. That’s a huge productivity
gain.”
33
Beryl Fleur, Head of Strategic Marketing at Carrefour,
adds: “With generative AI, we are confident of gaining significant
responsiveness, along with greater consistency and, of course,
budget effectiveness. For our teams, it is also the opportunity to
save time on repetitive tasks and focus more on creativity and
customer voice.”
34
28Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations using
generative AI have seen a
range of productivity
improvement
Furthermore, upon examining the spectrum of productivity
benefits, our research reveals that while the average
increase stands at 7.8%, organizations have achieved up
to a 25% boost in productivity over the past year through
the adoption of generative AI. Moreover, we observed that
the productivity gains tend to scale with the size of the
organization (see Figure 9). This is likely due to the higher
amount of investment made by these larger organizations, as
observed in Figure 9.
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 931 organizations that are at least exploring
generative AI capabilities.
Higher and lower productivity ranges are found by the 95th and 5th percentile respectively of individual productivity data. Average
productivity range is the statistical average across the entire sample.
Figure 9.
Generative AI yielded productivity benefits in the past yearProductivity improvement realized from generative AI in the past year, by organization size
Higher productivity range Average productivity rangeLower productivity range
$1 bn–$4.9 bn $5 bn–$9.9 bn$10 bn–$19.9 bnMore than $20 bnAverage
25.0%
7.8%
5.8%
6.6%
10.6% 11.0%
27.6%27.5%
22.0%
18.4%
0.5%
0.4%
0.5% 0.7%
1.2%
29Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations think
generative AI will shift
strategies and business
models
In our current research, over half of organizations (54%)
believe that generative AI has the potential to shift business
strategy fundamentally, up from 39% in 2023. Similarly, 40%
of organizations now believe that generative AI will push
them to review their business model, up from 22% last year
(see Figure 10).
Figure 10.
More organizations today realize the potential for generative AI to shift business strategies and models
% of organizations who agree with the statements below
Generative AI has the
potential to
fundamentally shift
business strategy
Generative AI will
require us to rethink our
business models in order
to remain competitive
39%
54%
22%
40%
2023 2024
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive
survey, May–June 2024, N = 1,031 organizations that are at least exploring generative AI capabilities.
30Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations believe that
generative AI will drive
revenue and innovation
A significant majority (74%) of organizations across sectors
agree that generative AI will drive revenue and innovation,
an increase from 60% last year. The high-tech sector
(96%) leads the way in acknowledging this (see Figure 11).
For example, Google launched virtual try-on technology
powered by generative AI, showcasing clothes on a variety
of models. With just one photo, it accurately displays how
the clothing fits and drapes on models of diverse skin tones
and ethnicities. Additionally, generative AI creates images
of customers wearing these items in various settings, such
as at the beach or a formal event, helping visualize product
suitability and boosting sales.
35
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey,
May–June 2024, N = 981 organizations that are at least exploring generative AI capabilities, excluding the public sector.
*Question asked in 2023: Do you see generative AI as an opportunity to drive revenue and innovation?
**In the 2024 average, the public sector is excluded, since it was not included in the 2023 research; in all 2024 data points respondents from
India are excluded, since they were not in the 2023 research.
Figure 11.
Organizations across sectors believe that generative AI will unlock revenue growth and foster innovation
% of organizations by sector who agree with the statement:
Generative AI is a transformative technology that will help us drive revenue and innovation
Average Telecom Pharma and
healthcare
Public sector/
government
Consumer
products
Automotive
High tech Energy and utilitiesAerospace and
defense
Industrial
manufacturing
Retail Financial
services
2023 2024
60%
74%
96%
76%
58%
77%
68%
76%
66%
75%
54%
44%
56%
45%
60% 59%
74% 74% 74% 74%
71%
69%
67%
31Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

To fully harness the power
of generative AI, three in
five organizations
acknowledge the need to
improve data foundations
Generative AI holds unprecedented potential to
extract value from data. However, many organizations
currently lack the robust data foundation necessary to
support it.
Our research reveals that nearly three in five
executives recognize that, in order to employ
generative AI to its full potential, there is a need
for significant alterations to data collection,
storage, retrieval, reusability, and governance.
Much data remains trapped in silos, with only half
of organizations possessing clear processes for
integrating data across functions. Only 51% of data
executives say that their organization has clear
processes to manage siloed data and data integration
across functions. Moreover, 49% of data sources are
in the cloud, with the remainder still residing in local
servers, posing accessibility challenges. Moreover,
most organizations (87%) have yet to utilize external
data sources for generative AI initiatives.
36
Organizations must fortify their data-governance
frameworks. Fewer than half (46%) have documented
policies around sourcing, usage, access, processing,
and security of data specifically for generative AI.
37
Crucially, 61% of data executives report their
organizations lack the necessary expertise to
transition into data-powered entities.
38
32Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

33Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

AI agents: The new
technology frontier 04
34Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Figure 12.
Transition of AI chatbots to multi-agent systems
AI chatbot
simulates and processes human
conversation, both written and spoken
AI agent
pursues complex goals autonomously,
showing independent decision making,
planning, and adaptable execution in
dynamic environments
Multi-agent system
multiple AI agents collaborating to
pursue complex goals autonomously in
dynamic environments
Conventional use of LLM Agentic use of LLM
Prompt Response
User User or proxy user
Prompt Response
Plan
Validate
plan
Search
Write
Reﬂect
Test
We define an AI agent as a technology designed to function
independently, plan, reflect, pursue higher-level goals, and
execute complex workflows with minimal or limited direct
human oversight. A multi-agent system is a collection of
these agents working together to solve tasks in a distributed
and collaborative way. Such systems exhibit characteristics
traditionally found exclusively in human operators, including
decision-making, planning, collaboration, and adapting
execution techniques based on inputs, predefined goals, and
environmental considerations.
As AI technology progresses, they will transition from the
role of supportive tool to that of independent agent with
full execution capability. Unlike conventional AI systems, as
well as following instructions, these agents can understand,
interpret, adapt, and act independently and, for certain tasks,
are capable of replacing human workers.
Source: Capgemini Research Institute analysis, Capgemini Applied Innovation Exchange – San Francisco.
35Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

AI agents promise smoother
automation and enhanced
productivity
Almost three-quarters (71%) of organizations anticipate
that AI agents will facilitate automation and a majority also
believe that they will relieve human operators of repetitive
tasks and allow them to focus on value-added functions such
as customer experience (see Figure 13).
Figure 13.
Organizations recognize significant value in AI agents
% of organizations who agree with the statements
AI agents will help us drive higher
levels of automation in our workﬂows
AI agents will signiﬁcantly improve
customer service, leading to improved satisfaction
AI agents would help me focus on more
value-added activities
The potential of AI agents to improve
productivity outweigh its risks
71%
64%
64%
57%
2024
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations who are at least
exploring generative AI capabilities.
36Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations anticipate the
arrival of AI agents
Our survey findings reveal that while only 10% of
organizations currently employ AI agents, a large majority
(82%) intend to integrate them within 1–3 years (see
Figure 14). Andrew Ng, founder of Deeplearning.AI, says:
"Agentic workflows let AI work iteratively, which yields a huge
improvement in performance. Agents can be used for robotic
process automation [RPA], but it is much bigger than that. We
will experience 'agentic moments,' when we see AI that plans and
executes a task without human intervention.”
39
Figure 14.
The next few years will see an uptake in adoption of AI agents
% of organizations using or planning to use AI agents
No plans to explore
Plan to use in 2-3 years
Plan to use within the next year
Already using
7% 30% 52% 10%
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 981 organizations who are at least
exploring generative AI capabilities, excluding the public sector.
*Figure excludes 1% that answered unsure/don’t know
37Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Figure 15.
Organizations plan to use AI agents across various areas
% of organizations who plan to use AI agents for the below activities
Generate, evaluate, provide feedback on
and rewrite code
Generate draft reports, incorporate
feedback, and iterate
Research, write, and optimize website content
75%
70%
68%
2024
The pharmaceutical and healthcare sector leads in AI agent
adoption (23%). Over the next year, a significant portion
of high-tech (77%) and retail (66%) organizations are
poised to embrace AI agents, indicating acceptance across
diverse fields.
AI agents offer organizations a versatility that will allow
deployment across various areas. About three-quarters
intend to deploy the technology for tasks such as generating
and iteratively improving code (see Figure 15).
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 846 organizations who plan to use
AI agents.
82
%
of organizations intend to use AI agents
within 1–3 years
38Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Despite the technology behind AI agents still being in its
infancy, some organizations are already tapping into the
potential of AI agents:
• LG's AI agent boasts robotic, AI, and multi-modal
technologies that enable mobility, active learning, and
engagement in complex conversations. It can manage
smart-home devices without user oversight, patrol the
home when no one is there, monitor the well-being
of pets, and generally enhance domestic security and
improve energy efficiency.
40
• Klarna, a Swedish payments company, uses an AI assistant
to handle tasks equivalent to the workload of nearly
700 employees. This AI assistant addresses service
requests, manages refunds, and handles returns in
various languages. According to Klarna, it has significantly
enhanced its efficiency and precision in resolving tickets,
cutting repeat inquiries by 25%. On average, it completes
tasks in one-fifth of the time that it took to do manually.
41
• Torq has incorporated a cybersecurity analysis AI agent
into its security hyper-automation platform, enabling
organizations to automate contextual alert triaging,
incident investigation, and response. This auto-
prioritization allows security staff to focus on urgent
matters, reducing stress levels and mitigating burnout.
Going forward, the technology will resolve 90% of tier-1
and tier-2 tickets independently.
42
Andrew Ng
Founder of Deeplearning AI
"Agentic workflows let AI work iteratively, which yields a huge
improvement in performance. Agents can be used for robotic process
automation [RPA], but it is much bigger than that. We will experience
'agentic moments,' when we see AI that plans and executes a task
without human intervention.”
39Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

What the future could
look like with AI agents
AI agents are reshaping business dynamics.
Microsoft founder Bill Gates says: ”Agents are
not only going to change how everyone interacts
with computers. They are also going to upend
the software industry, bringing about the biggest
revolution in computing since we went from
typing commands to tapping on icons.”
43
Source: Capgemini Research Institute analysis, secondary sources.
43, 44

Figure 16.
AI agents – how they work
Independently
plans
step-by-step
workﬂow,
moving from
subtasks to
execution of
complex tasks
Integrates
multi-modality
such as text,
voice, images,
video, etc.
Interprets
complex
dataset and
makes
contextual
decisions in
real time
Reviews and
corrects its
own output
Collaborates
with multiple
other agents
and third-party
applications ,
mimicking
real-world
environments
and situations
Executes
actions
towards set
goals with
minimal/no
human
oversight
40Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

While presently only a small share of organizations
use AI agents effectively, we envisage widespread
adoption across the following areas:
• Pharma/healthcare: AI agents will manage
and coordinate healthcare services, such as
appointment scheduling and patient care tasks,
including monitoring vital signs, administering
medication, monitoring elderly patients and
providing personalized care, reducing errors and
enhancing overall service delivery.
44
• Financial services: AI agents will continuously
monitor account activity to detect anomalous
transactions in real time, reducing fraud losses
and minimizing false positives.
45
• Customer service: AI agents will engage
customers with natural language, offering
personalized assistance and resolving queries
efficiently. This boosts customer satisfaction
and frees up human agents to focus on complex
tasks.
46
• Human resources: AI agents will manage administrative
tasks such as onboarding, payroll, benefits, and offer
proactive guidance. This allows staff to focus on strategic
initiatives and human interactions.
47
• IT service desk: AI agents will handle common
repetitive tickets, freeing staff up for complex tasks.
They can provide reminders, diagnose issues, search
systems, and take contextualized actions, reducing
resolution times and enhancing productivity.
50
• IT software development: AI agents will autonomously
develop complete software products, moving seamlessly
between stages, from analysis to monitoring, ensuring
quality assurance. This shifts the focus of software
engineers from routine coding to collaborating with
AI on human-centered design and navigating complex
systems.
51
• AI agents will reshape the human-machine-
interaction dynamic. However, broad adoption of
AI agents will give rise to significant ethical and
social concerns around privacy, , hallucinations, bias
accountability, and transparency. Addressing these
issues will demand robust governance frameworks,
ethical guidelines, and responsible AI practices to
maximize benefits while mitigating risks.
41Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations trust AI
agents – for specific tasks
In our survey, organizations expressed strong trust in AI
agents for specific tasks. A majority (63%) would trust AI
agents to analyze and synthesize data, while half would
trust it to compose work-related emails. Additionally, 60%
of organizations agree that, within the next 3–5 years, AI
agents will come to generate most of the coding within
organizations (see Figure 17).
Figure 17.
Organizations trust AI agents to execute specific tasks independently
% of organizations who agree with the statements below
I would trust an AI agent to
analyze and synthesize data for me
In the next 3–5 years, the majority of coding will be
generated by AI agents
I would trust an AI agent to send
a professional email on my behalf
63%
60%
50%
2024
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations who are at least
exploring generative AI capabilities.
42Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations recognize the
need to establish
governance and safety
mechanisms for AI agents
While AI agents have won the trust of executives for certain
tasks, organizations emphasize the need for safeguards.
More than half of organizations (57%) acknowledge the
necessity of instituting robust control mechanisms before
integrating AI agents into their operations. Organizations
also agree that humans must intervene in certain
circumstances. For example, 74% of surveyed organizations
agree that humans should produce a clear definition of a
given problem or task before entrusting it to AI agents.
Similarly, 73% assert that humans must verify decisions made
by AI agents and intervene when necessary.
43Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

The rise of small
language models
For the purpose of this research, small/
narrow language models (SLMs) are defined as
streamlined versions of large language models
(LLMs) and are characterized by reduced neural
network size and simpler architectures. In
contrast with LLMs, these models have fewer
parameters, require less data and training
time, and are targeted for specific industry- or
business- use cases rather than generic usage.
These smaller models also simplify the adoption
of generative AI on mobile devices, and through
on-premise or edge deployments.
Organizations are starting to recognize the
potential of SLMs. Among organizations in our
survey, 24% say they are currently using SLMs.
Over half (56%) plan to use them in the next
three years (see Figure 18).
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations who are at least exploring
generative AI capabilities.
*Figure excludes 2% that answered unsure/don’t know
Figure 18.
Nearly one-quarter of organizations already use SLMs
% of organizations using or planning to use small/narrow language AI models
No plans to explore Plan to use in 2-3 years
Plan to use within the next yearAlready using
18% 19% 37% 24%
44Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations like SLMs because they are cost-
effective, and are faster to develop, scale, and
tailor to specific needs, making them efficient
solutions for various industry- or business-specific
applications (see Figure 19).
“We have also recently seen that it is not only LLMs
with gigantic numbers of parameters (176 billion and
counting) that enable rich knowledge exchange. SLMs
with a comparatively puny 1.3 billion parameters,
which were trained on meticulously curated datasets,
give increased accuracy,” confirms Dhiman Basu
Ray, Global Chief Technology Officer, Digital
Engineering at Tech Mahindra.
52
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 627 organizations who are using/plan to use
small language models.
Figure 19.
Top reasons for preferring SLMs
Reasons organizations are using or considering using SLMs
Less expensive
Less computational and storage requirement
Quicker to train
Easier and more eﬃcient to develop
Easier and more eﬃcient to scale
Easier to tailor and ﬁne-tune for speciﬁc needs
76%
71%
69%
69%
63%
60%
2024
45Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

How organizations can
accelerate their generative
AI journeys 05
46Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Based on our global research and our experience working on
generative AI across industries and sectors, we recommend
key actions for advancing generative AI initiatives and
realizing its full potential. Organizations should accelerate
their journeys by following these steps:
Figure 20.
Key considerations for organizations to advance and scale generative AI initiatives
Establish a robust framework
for data governance and
management
Strengthen the data platform
and cultivate trust to ensure
reliable outcomes
Cultivate expertise through
strategic training and talent
development
Acquire
understanding and
expertise of the
generative
AI ecosystem
Deploy a generative AI
platform to manage
use cases at scale
Fortify against
cybersecurity
threats
Embrace emerging
trends such as AI agents
to boost competitiveness
and innovation
Source: Capgemini Research Institute analysis.
47Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Establish a robust framework for
data governance and management
Many organizations currently lack a solid data foundation
to leverage the full potential of generative AI. To rectify
this, organizations should establish new, robust data
infrastructure and governance protocols to underpin
generative AI initiatives:
• Ensure data fed into the AI/generative AI models follows
pre-defined protocols. Have documented policies on the
source, usage, access, and processing of data in generative
AI.
53
• Shape standardization and reusability policies for
generative AI use cases across multiple applications to
improve reliability and security and to reduce rework,
operating cost, and increase adoption between
functions.
54
• Establish a dedicated data-quality team to ensure that
only high-quality, up-to-date data from appropriate
sources comes into the generative process.
55
• Create a generative AI council tasked with making
informed decisions on data pilot requests. This council
will rigorously assess requests based on various criteria,
including cost, timelines, and data quality.
56
• Appoint a legal team with a strong technical
understanding of emerging legal issues, particularly
concerning intellectual property rights like copyright
law related to data used in generative AI initiatives. Our
research indicates that 64% of organizations cite legal
concerns (e.g., plagiarism, copyright infringements) as a
barrier to scaling generative AI beyond pilot projects.
48Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Strengthen the data platform and
cultivate trust to ensure reliable
outcomes
Our research underscores concern around transparency
and fairness in generative AI arising from doubt over of bias
leading to embarrassing outcomes, ambiguous training
data, and doubts about fairness (see Figure 21). Additionally,
as previously noted, there remains ambiguity regarding
the suitability of explanations offered by generative AI in
different contexts.
Figure 21.
Organizations face ethical challenges when implementing generative AI
Top ethical concerns of organizations in realizing the full potential of generative AI
Bias in the generative AI models
leads to embarrassing results when
used by customers/clients
Lack of conﬁdence that the
generative AI programs are fair
Lack of clarity on what underlying
data has been used to train the
generative AI programs
68%
64%
59%
2024
Source: Capgemini Research Institute, Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least
exploring generative AI capabilities.
49Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

But only 38% of organizations surveyed say they are deeply
considering the ethical implications prior to adoption.
Organizations should take the following steps to ensure
ethical standards are upheld
• Before forming partnerships, thoroughly evaluate the
vendor's data sourcing, management, security protocols,
and adherence to industry standards in order to head off
legal issues.
57
• Implement multiple steps to critically assess AI-generated
information to detect and mitigate data bias and regularly
review generative AI outputs for accuracy and factual
correctness.
58
• Identify and manage risks related to data confidentiality
and ethics by preventing unintentional ‘leakage’ of
sensitive data to public models.
59
• Fine-tune AI models to specific tasks while adhering to
international data-privacy guidelines.
• Train employees on responsible AI usage and regional or
industry-specific compliance.
• Stay updated on the latest advancements in generative
AI and understand their ethical implications. Consider
engaging with ethical AI networks such as AI Ethics Lab to
ensure widespread benefits while prioritizing safety for all.
• Understand legal and regional or cultural implications
for generative AI projects to ensure compliance with
legislative changes and adherence to expectations from
diverse end-users.
• Clearly communicate how customer data is managed for
business operations and detail security measures.
Asim Tewary, former Chief AI Officer at PayPal, comments:
" Customer-facing applications require much more effort than
internal use cases, in part because they are subject to far more
regulatory scrutiny. AI models must be more explainable (showing
how a system came to a decision) and traceable (showing what
data, processes, and artifacts went into the system). Additionally,
outputs must be validated to avoid hallucinations, or inaccurate
answers based on fabricated data.”
60
Matt Truppo, Global
Head of Research Platforms at Sanofi says “Transparency is
key. We have rolled out various training courses explaining the
fundamentals of AI to upskill our workforce. Additionally, we are
working on things like explainable AI to give insights into how the
predictions are made.”
61
50Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Cultivate expertise through
strategic training and talent
development
At the majority of organizations (70%) across sectors, limited
data and AI talent, skills, and knowledge is a major hindrance
to generative AI adoption at scale. Further, a recent global
work trend index revealed that, at organizations where
there is a perceived absence of appropriate training and
guidance, 78% of employees bring their own tools to work
and use them without managerial guidance or permission.
62

Our research reveals pervasive employee use of generative
AI in some capacity. However, simply allowing use does
not guarantee that most employees today are proficient
enough to maximize their potential contribution to
organizational improvements.
Leaders should take these steps to ensure teams are
prepared for generative AI:
• Ensure the appropriate degree of proficiency at all
level and roles. An entry-level employee, a seasoned
professional, a data expert, and an engineer will all need
different skills to incorporate AI into their day-to-day
work. A recent global work trend index report revealed
that 60% of leaders are apprehensive about the absence
of a clear plan and vision for AI implementation.
63
• Shift from theoretical education to practical application,
allowing employees to build up experience of using the
technology, building your organization’s reputation as
a generative AI “hothouse.” Thus, a key success factor
is to establish interdisciplinary, diverse teams to ensure
sustainable and inclusive generative AI integration.
• Create cross-functional project teams to ensure
development, varied experience, and points of view. For
example, including marketing or HR teams in technical
projects to add outside opinions and value.
• Carve out specialized learning courses in engineering and
coding-heavy roles. Topics could include deep learning
and neural networks, as well as training, maintaining, and
fine-tuning AI models.
• Finally, foster a synergy between humans and machines.
Soft human skills such as empathy, collaboration, critical
thinking, and complex decision-making will come to the
fore as complementary to generative AI’s technical speed
and volume proficiencies.
Officials at the US federal government are expediting the
hiring of AI professionals and aim to provide AI training for
employees at all levels in relevant fields. “With the emergence
of these tools over the past five years and then the most recent
explosion onto the public consciousness last year, there is
broad acknowledgment that the government needs to bring
in some of this in-demand skill set,” says Kyleigh Russ, Senior
Advisor to the Deputy Director of the US Office of Personnel
Management (OPM). Earlier this year, the US Department
of Justice hired its first chief AI officer to coordinate
the agency’s use of AI, foster AI innovation, and manage
AI-related risks.
64
Finally, encouraging employees and teams and creating
an environment free from the fear of failure is crucial to
unlocking the full potential of technology. “Get a team of
your best change-management experts to support the AI cultural
revolution. Do this as early as your first minimally viable product,”
recommends Helen Merianos, Head of R&D portfolio strategy
at Sanofi.
65
51Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Acquire understanding and
expertise of the generative AI
ecosystem
Various partners play unique roles, from AI services to
software development and advanced data analytics. Each
brings distinctive capabilities and competencies. While
an individual organization will struggle to find the full
range of resources required to capitalize on generative AI,
strategic partnerships combine complementary capabilities
and strategic knowledge to navigate the complexities of
developing and deploying AI-driven solutions, kickstarting
innovation and cutting time-to-market. “Collaboration
with technology companies is crucially important for us,” says
Volkswagen Chief Executive Officer Oliver Blume. “In the
future, we intend to simplify cooperation in organizational
and cultural terms.”
66
Nearly seven in 10 organizations (68%) in our survey plan to
use open-source or community generative AI models rather
than developing proprietary ones. Additionally, 64% favor
partnering with IT vendors and consulting/system integrators
(C&SI), while 61% prefer collaborating with startups. For
example, Sanofi is collaborating with OpenAI and Formation
Bio to boost its drug-development projects. The partnership
affords it access to proprietary data to develop AI models
for its biopharma business, while Formation Bio will provide
additional engineering resources.
67
Visa launched a $100
million generative AI fund to work with the next generation
of companies focused on developing generative AI
technologies and applications that will impact the future
of commerce and payments.
68
Unlike larger organizations equipped with extensive
resources and tools for developing customized AI
models for complex tasks, smaller organizations should
collaborate with external partners to enhance agility and
swiftly adopt generative AI. By doing so, they can gather
continuous feedback from employees and customers
to refine these models accordingly. Given their limited
budgets, smaller organizations can begin by implementing
less complex use cases, allowing them to discover value
while minimizing potential downstream impacts.
Specialist AI research organizations also offer a valuable
source of input. For example, Thales and the French
Alternative Energies and Atomic Energy Commission (CEA)
recently forged a new partnership. Over a renewable
three-year period, Thales will offer its AI expertise in the
defense and security sectors, while CEA will contribute its
expertise in multimodal generative AI, encompassing text,
images, audio, electromagnetic signals, structured data,
and other inputs.
Deploy a generative AI platform
to manage use cases at scale
To advance in their generative AI endeavors, organizations
can greatly benefit from deploying a generative AI
platform to manage use cases at scale. These platforms
facilitate effective scaling of generative AI initiatives,
ensuring flexibility, compliance, operational efficiency,
value tracking, and cost-effectiveness across diverse
business functions. Key advantages of such platforms
include:
Customization: Generative AI empowers organizations
to tailor solutions precisely to their specific needs and
challenges, whether enhancing personalized customer
experiences, creating customized products, or optimizing
operational workflows.
Guardrails: Generative AI platforms incorporate built-in
safeguards and guidelines that uphold ethical AI use,
ensure regulatory compliance, and mitigate biases, thereby
bolstering trust and reliability in AI-driven solutions.
Optimized cost efficiency: By automating processes and
enhancing predictive capabilities, generative AI platforms
help organizations streamline operations, minimize
waste, and optimize resource allocation. This optimization
translates to reduced costs across production, logistics,
and service delivery.
Value tracking: Generative AI platforms facilitate the
monitoring of value generated through each individual use
case throughout the end to end delivery lifecycle, allowing
for a precise prioritization of the use case portfolio against
the value generated for the relevant business function.
52Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

As highlighted previously, organizations have the option to
develop these platforms internally or leverage established
partnerships within the ecosystem to utilize external
platforms effectively. This strategic approach allows
organizations to harness the full potential of generative AI
while maximizing operational efficiency and innovation.
Fortify against cybersecurity
threats
Organizations face increasing threats from malicious
actors leveraging generative AI for sophisticated attacks,
intensifying risk landscapes. Our research indicates that
75% of organizations view cybersecurity risks as a barrier
to scaling generative AI. To mitigate these risks effectively,
organizations should implement the following measures:
• Implement robust protocols to identify and redact
sensitive data and intellectual property throughout all
stages from data collection, training, to inference.
• Strengthen security posture by embracing the Zero Trust
framework, limiting access to critical applications and
preemptively thwarting malware.
• Update security policies to promote the safe use of such
tools, clarifying acceptable practices and restricting
unauthorized tools. It is crucial to monitor and regulate
data access to prevent unauthorized or improper usage,
which could pose significant cybersecurity risks.
• Regularly update and reinforce employee training
programs to educate staff on the evolving nature of cyber
threats powered by generative AI. Focus areas should also
include recognizing sophisticated phishing attempts and
deepfake content. Nearly seven in 10 (69%) organizations
express significant concern over the potential use of
deepfakes for malicious content targeting specific groups
or organizations.
• Stay updated on evolving AI regulations (e.g., GDPR, EU
AI Act) and integrate compliance measures to protect
sensitive data.
In addition, generative AI also presents opportunities to
strengthen cybersecurity defenses. Leveraging these
tools can enhance threat detection, automate incident
response, bolster threat intelligence capabilities, and support
overburdened security teams, enabling them to focus on
strategic initiatives and proactive defense measures.
Assaf Keren, former Chief Information Security Officer
and Vice-president of Enterprise Cybersecurity at PayPal,
comments: “While generative AI may be used by attackers for
malicious purposes such as generating false identities or creating
malware variants that evade traditional security measures, it
will also empower organizations to explore generative AI-driven
defense mechanisms, such as next-generation automated threat
detection systems and response capabilities. With generative
AI, there will be opportunities to augment capabilities, remove
friction, and drive greater customer value.”
69
Embrace emerging trends such as
AI agents to boost competitiveness
and innovation
Tapping into the latest AI technology will inject energy
and catalyze innovation in organizational R&D. AI agents
can fit quickly into an enterprise environment. “AI agents
will transform the way we interact with technology, making it
more natural and intuitive. They will enable us to have more
meaningful and productive interactions with computers,”
comments Fei-Fei Li, Professor of Computer Science at
Stanford University.
70
Organizations are poised for a future
where human employees work alongside AI agents in a
seamless hybrid workforce. This symbiotic relationship will
streamline operations and enhance service delivery, boosting
productivity and operational efficiency.
However, organizations cannot proceed without caution.
Oversight of what AI is doing – its decision-making process
and accountability for output remains a key organizational
responsibility. Given their autonomy, AI agents are even
harder to control than single agents; therefore, it is critical
that organizations have sufficient standards and controls in
place to enhance trust in the technology before deploying.
Accountability remains with humans.
53Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

As investment goes into the development of generative AI
and adoption increases, it becomes more significant as both
a business and cultural phenomenon. Early adopters are
receiving tangible benefits in efficiency and productivity,
driving strategic pivots in business models. Additionally,
the emergence ofnovel tools and techniques like AI agents
marks a shift that requires organizations to re-evaluate their
strategic investments.
Looking ahead, organizations must establish a robust
framework for data governance and management to ensure
integrity and compliance. Strengthening the data platform is
essential for reliable outcomes and building stakeholder trust.
Conclusion
Deploying a generative AI platform will facilitate the effective
implementation and management of use cases at scale
across the organization. Enhancing cybersecurity defenses
and fostering expertise through targeted training are
crucial steps. Embracing innovations like AI agents will drive
impactful advancements and secure a competitive edge.
These are exciting times for business, with new tools
and efficiencies appearing all the time. Leaders should
proceed with open minds and a willingness to experiment
and scale, while focusing on trust, but also a sense of
responsibility as they lead their organizations to the new
technological frontier.
54Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Research methodology
We conducted a global survey of 1,100 executives at
organizations with more than $1 billion in annual revenue
across 14 countries: Australia, Canada, France, Germany,
Italy, India, Japan, the Netherlands, Norway, Singapore,
Spain, Sweden, the UK, and the US. Organizations operate
across 11 sectors; nearly all (94%) of these organizations
Organizations by annual revenue
1%
1%
44%
44%
24%
24%
14%
14%
18%
18%
Organizations by annual revenue
More than $20 bn
$10 bn–$19.9 bn
$5 bn–$9.9 bn
$1 bn–$4.9 bn
$500 mn–$999 mn
2024
2023
have started to explore generative AI. The global survey
took place in May and June 2024. Executives surveyed
are at director level and above and represent diverse
functions.
The study findings reflect the views of the respondents to
our online questionnaire for this research and are aimed
at providing directional guidance. Please contact one of
the Capgemini experts listed at the end of the report to
discuss specific implications.
55Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Respondents by gender, 2024
64%
36%
Men Women
Respondents by gender, 2024
Organizations by number of employees
39%
23%
14%
6%
3%
3%
5%
7%
Organizations by number of employees
More than 100,000
75,000–100,000
60,000–75,000
45,000–60,000
30,000–45,000
15,000–30,000
5,000–15,000
Less than 5,000
202450%
50%
45%
42%
3%
8%
Respondents by title
Executive
Vice president
Director
2024
2023
Respondents by title
56Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Organizations by location of headquarters
15%
15%
10%
10%
10%
10%
10%
10%
8%
7%
8%
7%
8%
6%
6%
6%
6%
6%
5%
5%
5%
5%
4%
4%
4%
4%
7%
Organizations by location of headquarters
India
Norway
Sweden
Singapore
Australia
Italy
Japan
Canada
Netherlands
Spain
France
Germany
United Kingdom
United States 2024
2023
Organizations by sector
15%
15%
15%
15%
15%
15%
10%
10%
10%
10%
10%
9%
9%
5%
5%
5%
5%
5%
5%
5%
10%
Organizations by location of headquarters
Public sector government
High tech
Retail
Pharma and healthcare
Industrial manufacturing
Consumer products
Aerospace and defense
Telecom
Automotive
Energy and utilities
Financial services 2024
2023
Respondents by function
21%
2024
17%
13%
10%
10%
9%
6%
6%
6%
2%
1%
Respondents by function
Human resources
Government/citizen services
Manufacturing/ production/operations
Sales
Procurement/supply chain/logistics
Finance and risk
Sustainability
Marketing
Corporate strategy
Technology/digital
Innovation/R&D/ product design
57Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Appendix
Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
IT
Generation of synthetic data
Improve IT service delivery through chatbots and virtual assistants
Auto-generation and completion of data tables
Generation of code and automated testing
Facilitate learning of new programming languages
Resolve coding errors and bugs
Cybersecurity threat detection in real time
Data access and management (both internal and customer data)
32%
31%
28%
27%
-
-
-
-
2%
3%
4%
3%
-
-
-
-
35%
42%
36%
46%
39%
40%
44%
42%
32%
24%
27%
26%
26%
25%
24%
28%
33%
25%
32%
35%
31%
5%
3%
5%
4%
2%
39%
40%
37%
37%
40%
17%
32%
26%
26%
25%
Optimize and streamline sales operations
Cross-selling opportunities
Optimize sales support chatbots
Virtual assistants for customer engagement
Improve sales team performance through real-time feedback, risk ﬂagging,
and recommending next interactions
Sales

Gener ative AI use c ases b y function
Continue to the next page...
58Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Marketing
Create and optimize marketing campaigns
Improve customer segmentation and targeting
Real-time customer feedback analysis
Personalized content generation and product recommendations
Sentiment analysis across diﬀerent marketing channels
30%
30%
30%
31%
-
2%
3%
2%
1%
-
36%
37%
41%
46%
38%
21%
18%
13%
24%
18%
32%
30%
24%
27%
21%
39%
22%
21%
25%
19%
-
2%
2%
3%
2%
2%
-
38%
39%
37%
39%
38%
41%
27%
23%
25%
27%
29%
24%
2%
1%
4%
3%
2%
42%
48%
44%
42%
37%
18%
11%
14%
24%
27%
Generate new design concepts/conﬁgurations
Quality control and fault detection
Product testing and validation processes
Optimize product performance and functionality
Compose entirely new materials to target speciﬁc physical
properties (material science)
Optimize supply chain planning and management through predictive analytics
Improve warehouse management through automated inventory tracking
Reduce downtime through predictive maintenance
Reduce transportation costs through route optimization
Industrial robot and machine reprogramming
Scenario planning to prepare for operational disruptions
Product design/
research and
development/
manufacturing
Logistics/
operations
59Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Risk management
Improve risk assessment and prediction through real-time data analysis
Summarize and highlight changes from large documents (e.g., annual reports)
Draft and review legal and regulatory documents
Answer queries by analyzing sensitive information from legal documents
24%
21%
22%
26%
5%
3%
3%
2%
35%
43%
38%
41%
30%
22%
26%
27%
16%
32%
26%
38%
34%
-
-
-
-
-
-
-
-
-
-
-
40%
43%
39%
41%
45%
26%
23%
18%
25%
20%
8%
1%
5%
1%
4%
-
41%
40%
40%
36%
43%
37%
27%
25%
27%
26%
23%
24%
Invoice processing
Budgeting, cash ﬂow forecasting
Tax compliance
Financial reporting
Fraud detection
Investment analysis
Automate tracking of energy-usage data
Automate tracking of carbon emissions data
Automate reporting for regulatory disclosures
Automate tracking of waste-management processes
Monitor water usage in facilities for water management
Finance
ESG/sustainability
60Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Human resources
Automate documentation process
Analyze data for people analytics
Employee engagement with chatbots and virtual assistants
Optimize and expedite performance management
Automate learning and development procedure
Content creation for recruitment, enhancing job postings, and
candidate communication
Content summarization for internal tasks and processes
Virtual administrative assistant for claims and case managers
Self-service bot for citizen services
Content generation for government communication
-
-
-
-
-
-
-
-
-
-
-
-
-
-
24%
38%
31%
17%
24%
38%
31%
17%
-
-
-
-
-
-
37%
44%
36%
41%
39%
44%
28%
21%
24%
23%
24%
24%
Government
services
Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities; N varies per functional use case ranging from 499 to 716.
* “Implementation” means organizations that have partially scaled the functional use case in question.
61Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Automotive
Consumer
products and
retail
Enhance autonomous vehicle development
Generate scenarios and test them for safety and performance in a
simulated environment
Customize vehicle features and conﬁgurations to individual
customer preferences
Generative design of parts optimized to meet speciﬁc goals and constraints
(e.g., fuel eﬃciency, innovative lighter design)
Improve in-vehicle experience for the customers
Sales and after sales customer engagement
Build creative marketing campaigns and visually appealing advertisements
Hyper personalized, multi-sensory, consumer experience
Find niche audiences to survey
Match brand visions with content creators
Eﬃcient, self-optimizing real-time customer-service chatbots
Predictive analysis of forecast demand and consumer trends
Inventory management
22%
25%
24%
23%
12%
20%
3%
1%
2%
2%
4%
4%
39%
35%
48%
39%
32%
33%
26%
29%
17%
23%
19%
21%
27%
37%
24%
22%
33%
-
-
0%
2%
2%
2%
7%
-
-
37%
38%
39%
33%
43%
40%
34%
23%
30%
25%
30%
27%
25%
21%

Generative AI use cases by sector
62Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Energy and
utilities
Financial services
Generation of synthetic data
Inverse design, composing entirely new materials to target speciﬁc physical
properties (material science)
Smart-grid management
Predictive maintenance to prevent equipment failures
Generation of synthetic customer data
Accurate price forecasting and portfolio optimization
Improved prospect proﬁling and customized product recommendations
for account managers
Claims processing, mortgage processing
Fraud detection
Risk assessment and management
Credit scoring and loan approval
Personalized ﬁnancial planning and advisory services
Regulatory compliance and reporting
Customer service automation through chatbots and virtual assistants
33%
24%
-
-
1%
3%
-
-
20%
32%
28%
37%
30%
31%
31%
19%
34%
27%
31%
31%
25%
-
-
-
-
-
6%
3%
8%
1%
7%
-
-
-
-
-
42%
45%
40%
41%
46%
24%
37%
34%
38%
44%
17%
34%
24%
30%
16%
21%
15%
22%
21%
14%

63Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Pharma and
healthcare
Telecom
Replication of structured data (e.g., EHR, claims, registries, clinical trials)
Generation of synthetic data for research and analysis
Generate, predict, and understand biomolecular data
Advance drug discovery and therapeutics
Design of novel proteins
Personalized treatment/medicines for better patient care with MedTech
Facilitate clinical trials and research
Facilitate medical training and simulation for healthcare
professionals and students
Create new personalized services and oﬀerings, and
customer-service interactions
Generative network design and architectures that identify the most eﬃcient
and eﬀective network conﬁguration
Restoration of old media
Call-center analytics
Text to video generation, ﬁlmmaking
22%
27%
32%
35%
30%
-
-
-
28%
28%
30%
36%
21%
0%
2%
0%
2%
6%
28%
28%
27%
37%
27%
18%
32%
31%
24%
27%
3%
0%
2%
2%
2%
-
-
-
24%
23%
22%
26%
25%
44%
29%
35%
39%
33%
28%
28%
28%
20%
29%
28%

64Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Function Use case
2023
Pilot/POC Implementation Pilot/POC Implementation
2024
Manufacturing
Public sector/
government
Generative design of parts optimized to meet speciﬁc goals and constraints
Inverse design, composing entirely new materials to target speciﬁc physical
properties (material science)
3D modeling to create detailed shapes
Predictive maintenance
Robot programming
New product development and prototyping
Personalization of user experience
Development and deployment of autonomous vehicles and drones
Scenario planning for decision-making process
Public health monitoring and resource allocation
Analytics for eﬃcient government procurement
Emergency response and crisis management
Virtual assistants for government services
Fraud detection
37%
39%
42%
33%
29%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
54%
23%
40%
28%
26%
13%
23%
17%
31%
33%
6%
1%
6%
3%
2%
-
-
-
-
35%
32%
37%
39%
34%
28%
30%
30%
35%
25%
30%
17%
24%
23%
23%
25%
26%
26%

Source: Capgemini Research Institute, Generative AI executive survey, April 2023, N = 800 organizations; Generative AI executive survey, May–June 2024, N = 1,031 organizations that are at least exploring
generative AI capabilities; N varies per sector use case ranging from 50 to 189.
*" Implementation” means organizations that have partially scaled the functional use case in question.
65Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

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five-year strategic partnership to accelerate cloud and
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financial advisors speedy access to a database of about 100,000
research reports and documents,” September 2023.
19. AWS Startup Blogs, “How climate tech startups use generative
AI to address the climate crisis,” February 2024.
20. Rockwell Automation, "Industrial organizations will benefit
from industry-first generative AI for automation design,”
October 2023.
21. Schneider Electric, "Schneider Electric drives generative
AI productivity and sustainability solutions by integrating
Microsoft Azure OpenAI,” November 2023.
22. CIO, "UPS delivers customer wins with generative AI,” May 2024.
23. Nvidia, “Insilico Medicine uses generative AI to accelerate
drug discovery, enters phase 2 clinical trials,” June 2023.
24. Moderna, “Moderna and OpenAI collaborate to advance
mRNA medicine,” April 2024.
25. AP News, "California to tap generative AI tools to increase
services access, reduce traffic jams," May 2024.
26. Gov Tech, "Pennsylvania announces gen AI pilot for state
employees," January 2024.
27. Azure, “Our customers can use search, visual search, and
browsing to find our products, but we wanted to try
something that was conversational,” February 2024.
28. CNBC, “A new Walmart in-store AI is giving employees advice
on how to sell products before it’s too late,” April 30, 2024.
29. Korean Times, “Baskin-Robbins opens AI lab to develop
‘innovative’ flavors,” February 2024.
30. AT&T, “We’ve worked with Microsoft to make Ask AT&T
secure and safe for our employees and our corporate data,”
June 2023.
31. Vodafone, "VOXI by Vodafone launches generative AI
chatbot to enhance customer experience," March 2024.
32. Mercedes-Benz, “Mercedes-Benz heralds a new era for the
user interface with human-like virtual assistant pow-ered by
generative AI,” January 2024.
66Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

33. Celonis, “Retailer Carrefour is transforming procurement with a
process approach to generative AI,”
34. Horizons, “Gen AI at work: The story of Carrefour Marketing
Studio,” February 2024.
35. Forbes, “7 ways retailers are using generative AI to provide a
better shopping experience,” February 2024.
36. Capgemini Research Institute, Data-powered enterprises
survey, April 2024, N = 500 organizations represented by 500
data executives and 500 business executives.
37. Ibid.
38. Ibid.
39. O’Reilly Media, “Generative AI in the real world: Andrew Ng on
where AI is headed. It’s about agents,” May 2024.
40. LG Electronics, "LG ushers in ‘Zero Labor Home’ with its smart
home AI agent,” December 2023.
41. Forbes, “Klarna's new AI tool does the work of 700 customer
service reps,” March 2024.
42. MSSP Alert, "Torq adds AI agent to security hyper automation
platform," August 2023.
43. PC Mag, “Bill Gates: AI is about to completely change how you
use computers,” November 2023.
44. Deeplearning.ai, accessed in June 2024.
45. Moveworks, “How agentic AI is driving the next evolution
of enterprise AI,” February 2024.
46. Ibid.
47. Ibid.
48. Ibid.
49. Ibid.
50. Ibid.
51. Forbes, “Will AI agents replace software engineers' jobs?”
June 2024.
52. Tech Mahindra, “Generative AI: A journey towards
sustainable and human-centric productivity,” May 2024.
53. Capgemini Research Institute, Data-powered enterprises
survey, April 2024, N = 500 organizations represented by
500 data executives and 500 business executives.
54. Capgemini, “Generative AI – built for business,” accessed in
July 2024.
55. Capgemini Research Institute, Data-powered enterprises
survey, April 2024, N = 500 organizations represented by
500 data executives and 500 business executives.
56. Ibid.
57. Ibid.
58. Ibid.
59. Ibid.
60. MIT, “Financial services’ deliberate approach to AI”, May 2024.
61. Sanofi, “Exploring our digital transformation: strategies and
innovations,” March 2024.
62. LinkedIn, “A new framework for AI upskilling across your
organization,” May 2024.
63. Ibid.
64. CIO, “US government enters the race for AI talent,” March 2024.
65. Medium, “AI and automation: Helen Merianos of Sanofi on how
to effectively harness AI technology in people operations,”
October 2023.
66. Techmonitor, “Volkswagen Launches AI Lab,” February 2024.
67. Reuters, “Sanofi partners with OpenAI, Formation Bio on
AI-driven drug development,” May 2024.
68. Visa, “Visa Launches $100 million Generative AI Ventures
Initiative”, October 2023.
69. Venturebeat, “PayPal’s CISO on how generative AI can improve
cybersecurity,” November 2023.
70. Skim AI, “10 quotes on AI agents from the top industry experts,”
June 2024.
67Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Key contributors
Dr. Mark Roberts
Deputy Director of Capgemini Generative
AI Lab, CTO Applied Sciences, Capgemini
Engineering
[email protected]
Pascal Brier
Group Chief Innovation Officer,
Capgemini
[email protected]
Alex Marandon
Vice President, Global Head of Generative
AI Accelerator, Capgemini Invent
[email protected]
Mark Oost
Global Offer Leader AI, Analytics,
and Data Science, Insights and Data,
Capgemini
[email protected]
Lisa Mitnick
Executive Vice President, Americas
Portfolio Lead, Capgemini
[email protected]
Steve Jones
Executive Vice President, Data Driven
Business and Generative AI, Capgemini
[email protected]
Anne-Laure THIBAUD (THIEULLENT)
Executive Vice President, Data, AI & Analytics
Group Offer Leader
[email protected]
Marisa Slatter
Director, Capgemini Research Institute
[email protected]
Hiral Shah
Manager, Capgemini Research Institute
[email protected]
Jerome Buvat
Head of the Capgemini
Research Institute
[email protected]
68Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

The contributors would like to especially thank
Subrahmanyam KVJ, Amol Khadikar, Anjali Roy, and Siva
Chidambaram for their contributions to the research.
The contributors would also like to thank, Marek Sowa,
Daniela Rittmeier, Ayan Bhattacharya, Oliver Jones, Philipp
Fürst, Yashowardhan Sowale, Eric Reich, Ashvin Parmar,
Christopher Scheefer, Neeraj Abhyankar, Myriam CHAVE,
Ether Buck, Jaydeep Neogi, Vibha Palekar, Suparna
Banerjee, Mithun Duttu, and Amitava Duttu for their
contributions to the research.
About the Capgemini
Research Institute
The Capgemini Research Institute is Capgemini’s in-house
think tank on all things digital. The Institute publishes
research on the impact of digital technologies on large
traditional businesses. The team draws on the worldwide
network of Capgemini experts and works closely with
academic and technology partners. The Institute has
dedicated research centers in India, Singapore, the United
Kingdom, and the United States. It was recently ranked
number one in the world for the quality of its research by
independent analysts.
Visit us at www.capgemini.com/researchinstitute/
Regional
APAC
Europe
Financial Services
North America
India
NEERAJ ABHYANKAR
[email protected]
RAÚL BARTOLOMÉ RUIZ
[email protected]
RAJESH IYER
[email protected]
AYAN BHATTACHARYA
[email protected]
BIKASH DASH
[email protected]
Global
MARK OOST
[email protected]
CONOR MCGOVERN
[email protected]
ROBERT ENGELS
[email protected]
NARESH KHANDURI
[email protected]
MAREK SOWA
[email protected]
SERGE BACCOU
[email protected]
For more information, please contact:
69Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Delivering trusted business value with Gen AI at scale
Gen AI is already fueling digital transformation in many
sectors, leading to performance improvement as well as
next-generation customer engagement and personalized
experiences. While these large models are undoubtedly
powerful, it is essential to implement safeguards before fully
integrating them into your business operations.
Building on our market leading position in Data and AI, we
help our clients deliver value and generate competitive
advantage by leveraging trustworthy Gen AI at scale.
We support CXOs in setting their Gen AI strategy and
identifying use cases aligned to their business expectations
and requirements, offering a portfolio of tailored Gen
AI solutions.
Our Capgemini Group portfolio provides a comprehensive set
of Gen Ai offerings and capabilities to deliver value:
Most Gen AI maturity journeys start at the same point,
organizations need trust, cost, and scale controls in a
single toolkit. They need an efficient, interoperable, and
scalable Gen AI development framework to navigate and
orchestrate in the fast-evolving environment. To support the
industrialization of your custom Gen AI projects we have built
a Reliable AI Solution Engineering (RAISE). Focusing on value
cases across industries, RAISE is an operational accelerator
to deliver your Gen AI initiatives at scale with needed
guardrails, giving you more reliable and tangible results, at a
controlled cost.
RAISE will bring the battle-tested toolkit for your custom Gen
AI projects:
• Cost efficiency: Up to 80% lower inference/run cost per
application vs. call to single massive LLM
• Scale ready: Up to 60% faster deployment vs.
disconnected and siloed build & deploy
• Trusted AI: Up to 40% faster issue detection vs. manual
monitoring
Learn more about our Generative AI offerings here: https://
www.capgemini.com/services/data-and-ai/generative-ai/
GENERATIVE AI STRATEGY
CUSTOM GENERATIVE AI FOR ENTERPRISE
Deﬁne and prioritize the most relevant Gen AI use cases for business, and lay the right foundations in terms of people, process, and
technology as well as a compelling business case to deliver business outcomes at the right cost.
Move beyond the generic models and solutions with custom models created speciﬁcally to address your use case and industries.
Industrialize and accelerate custom Gen AI deployments in a cost-eﬃcient manner, with the relevant guardrails to ensure
trusted outcomes.
Personalize every customer experience
and empower agent eﬃciency across
sales, service, commerce, and marketing.
Improve eﬃciency and quality across
the software lifecycle, accelerate the
time to market for new software and
improve security.
Enhance software products with
hyper-personalized experiences,
improve operational eﬃciency, and
enable scalable growth.
GENERATIVE AI FOR
CUSTOMER EXPERIENCE
GENERATIVE AI FOR
SOFTWARE ENGINEERING
GENERATIVE AI FOR
SOFTWARE PRODUCT ENGINEERING
70Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

More Capgemini
Research Institute Publications
Harnessing the value of
generative AI: Top use cases
across industries
Why consumers love generative AI The resurgence of manufacturing:
Reindustrialization strategies in
Europe and the US
Digital twins: Adding intelligence
to the real world
Building the next-gen pharma lab:
Digitally connected, environmentally
sustainable
Data for net zero: Why data is key to
bridging the gap between net zero
ambition and action
The Eco-Digital Era
TM
: The dual
transition to a sustainable and
digital economy
Generative AI and the evolving
role of marketing: A CMO’s
playbook
71Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Receive copies of our reports by scanning
the QR code or visiting
https://www.capgemini.com/capgemini-research-institute-subscription/
Subscribe to latest research
from The Capgemini Research Institute
72Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

NOTES
73Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

NOTES
74Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

NOTES
75Capgemini Research Institute 2024
Harnessing the value of generative AI: 2
nd
edition Top uses cases across sectors

Copyright © 2024 Capgemini. All rights reserved.
About Capgemini
Capgemini is a global business and technology transformation partner,
helping organizations to accelerate their dual transition to a digital and
sustainable world, while creating tangible impact for enterprises and
society. It is a responsible and diverse group of 340,000 team members in
more than 50 countries. With its strong over 55-year heritage, Capgemini
is trusted by its clients to unlock the value of technology to address the
entire breadth of their business needs. It delivers end-to-end services and
solutions leveraging strengths from strategy and design to engineering, all
fueled by its market leading capabilities in AI, cloud and data, combined
with its deep industry expertise and partner ecosystem. The Group
reported 2023 global revenues of €22.5 billion.
Get the Future You Want | www.capgemini.com

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